




































AGORA International Journal of Economical Sciences, http://univagora.ro/jour/index.php/aijes 

ISSN 2067-3310, E-ISSN 2067-7669 

Vol. 17, No. 2 (2023), pp. 87-98 

 

87 

 

ESTIMATION OF REGIONAL INNOVATION ACTIVITY 
 

A. HUSEYNOVA, O. MAZANOVA 

 

Arzu Huseynova1, Ophelya Mazanova2 

Azerbaijan State University of Economics (UNEC), Azerbaijan  
1 ORCID No. 0000-0002-0981-9923, E-mail: arzu_huseynova@unec.edu.az 
2 ORCID No. 0000-0001-7344-3492, E-mail: ofelya.mazanova@unec.edu.az 

 

Abstract: In the article, the estimation of the regional innovation activity, the general 

methodology of the estimation of the innovation development of the regions in this direction were 

analyzed by the author. The innovation index was formed based on the principles accepted in the 

international world to assess the innovation potential. According to the comparative evaluation of 

Azerbaijan's innovation potential, an innovation index was found for each region, zones were ranked 

according to this index, and cluster analysis was conducted. The selected system of indicators allows 

us to evaluate the level of innovation development in different areas, and to analyze the factors 

affecting the innovation index in the regions. 

Keywords: Azerbaijan, innovation, regional innovation, global Innovation Index. 

 

INTRODUCTION 

Today, the formation of the innovation structure in the regions in Azerbaijan is still at the 

initial stage. The assessment of the regional innovation system (RIS) is still in the formative stage. 

Various scientists (A.Huseynova, and T.Aliyev) have investigated and evaluated the methods of 

regional innovation activity assessment in the republic in their studies. 

The existence of numerous approaches to the assessment of RIS is due to the complexity of 

its structure. A special system of indicators should be developed to reveal the internal structure of the 

region's innovation-oriented economic system and to evaluate the interaction mechanisms of its main 

elements. 

The main goal is to identify a more effective regional innovation system by conducting an 

estimation. 

The "European Innovation Scoreboard" methodology, which we consider as a basis, 

determines the information source, the composition of criteria and indicators, organizational ways, 

and common rules for the analysis and evaluation of the scientific and technical complex on the basis 

of the innovation index. 

 The "European Innovation Scoreboard" methodology, which we take as a basis, determines 

the information source, the composition of criteria and indicators, organizational ways, and general 

rules for the analysis and evaluation of innovation potential on the basis of the innovation index. This 

methodology consists of 4 stages. 

 

 

 

 

 

mailto:arzu_huseynova@unec.edu.az
mailto:ofelya.mazanova@unec.edu.az


ESTIMATION OF REGIONAL INNOVATION ACTIVITY 

88 

 

Figure 1. Let's explain each stage separately 

 
Figure 1. Indicator system of innovation activity 

Source: author’s work 

 

 
 

Figure 2. Stages of the methodology 

Source: author’s work 

 
 

Source: author’s work 

 

 In our case, regions are taken as objects. In fact, we consider states, ministries, organizations, 

research institutes, universities, etc. as objects. It depends on the existing issue. As mentioned, the 

system of indicators characterizes the innovation potential and socio-economic environment of the 

region. All indicators correspond to the statistical system (Huseynova, A., Mazanova, O. , 2013). 

During the development of the methodology, the development of innovation, the indicators of the 

socio-economic environment, their interrelationship and complex compatibility, the proposed 

indicators, and the methods of evaluation and analysis with the application of the system of indicators 

were considered. 

In the first stage, the selection and collection of required indicators are carried out to calculate the innovation 
index . As we mentioned before, the indicators are grouped according to their characteristics.

In the second stage, indexes are calculated and summed up according to each group of indicators.

In the third stage, regions are ranked according to the value of the innovation index, and similar ones are 
selected according to the level of scientific and technical development according to the cluster analysis.

The fourth stage is the construction of a regression model that determines the influence of socio-economic 
environment factors on the formation of the innovation index.

Innovation index 

Socio-economic factors of the scientific and technical environment 

Information 

provision 

Living 

standard of 

the 

population 

Educational 

environment 

Infrastructu

re 

developme

nt 

Economic 

developm

ent 

Reserves of activity The scale of the 

activity 

Results of action 

I stage II stage III stage IV stage 

The formation 

of the initial 

database 

Determination 

of innovation 

index 

Ranking 

according  to 

innovation index 

Producing a 

regression model 

(factor analysis) 



Arzu HUSEYNOVA, Ophelya MAZANOVA 

 

89 

 

METHODOLOGY AND ANALYSİS 

 The tool of this methodology is the multidimensional statistical method. We used the SPSS 

17 statistical package and MS Excel spreadsheet as economic modeling tools. First of all, the used 

indicators are made comparable, in other words, a single scale of indicators is created. 

Normalization of indicators is carried out by linear scaling methodology: 

 

minmax

min

GG

GG
G i

nor



         (1) 

 

where Gnor – is the normalized value of the indicator; Gi – is the initial value, Gmin and Gmax – are the 

smallest and largest values, respectively. 

 The linear transformation procedure scales the data. All quantities are located in the interval  

[0; 1]. Such data is easy to interpret. The normalization procedure does not affect the results of the 

analysis, since our goal is a qualitative assessment based on the examination of numerical indicators. 

 The normalized values of the indicators are combined in the first level indicators 

corresponding to their functional structure. For example: first, the average value of the normalized 

indicators for subgroups is determined, and then a special index for the group is determined. In other 

words, the special index of the group ("Reserves") is calculated according to the average value of the 

normalized indicators of the "Labor reserves" and "Materials and technical base" subgroups of the 

"Reserves" group (Appendix 2). 

Average indicators for groups ( jG , j=1,2,3 indicate groups) are calculated by the following formula: 

n

G

G

n

i
i

j


 1

         (2) 

where Gi - is the i-th indicator included in the group, n- s the number of indicators. It forms the basis 

of the resulting ranking and cluster analysis. Special indices obtained jG  by groups allow to determine 

the innovation index. Special indices obtained by groups allow to determine the innovation index.The 

innovation index (I) is calculated as follows: 

 

3

321 GGG
I


          (3) 

where jG - j=1,2,3 is the average price for groups. 

 The analysis of the division of objects is carried out according to the system of indicators 

selected on the basis of the reports on the standardization of indicators. Based on the ranked set of 

economic zones, they are grouped into clusters. 

 The cluster method is a multidimensional statistical procedure (Arzu Huseynova, 2022, pp. 

867-875). This method arranges the objects in groups according to relatively similar characteristics 

based on the available information. The cluster analysis method consists of several steps: 

 



ESTIMATION OF REGIONAL INNOVATION ACTIVITY 

90 

 

Figure 3. Steps for the cluster analysis method 

 
Source: author’s work 

 

 In our case, since the number of objects is small, the hierarchy algorithm Ward method is 

chosen. The method of analysis allows us to analyze the factors. Here, too, indicators are initially 

normalized. Then they are checked.  

 Socio-economic factors can have both positive and negative effects on the environment. 

Therefore, they should be divided into two groups accordingly. The initial data prepared in this way 

can be used in the construction of the regression model. Then, the formation of a correlation model 

to determine the effect of the factor indicators on the final signs is necessary. 

 In the modeling process, the important indicators for the final signs are determined. The 

structure given for economic zones is divided into stages corresponding to the functional structure of 

the factor indicator. In the first stage, the influence of the education level on the innovation index; the 

level of information provision infrastructure elements of the region; standard of living; level of 

economic development, etc. the parameters of the reflective regression equation are calculated. The 

next stage of modeling is the calculation of variance and coefficient of determination for each factor 

characteristic. Based on these coefficients, a decision is made to include special indicators in the 

regression model and a pair regression model is built for each cluster. This model allows predicting 

the value of the innovation index, which depends on the change in the values of the factor indicators. 

Calculations were made according to two main methods:  

 

Figure 4. Methods for innovation development regions model 

 
Source: author’s work 

 

 Both methodologies are based on the system of indicators characterizing the internal and 

external environment and socio-economic factors of RIS. The proposed methods use widely applied 

tools in the international world. In order to evaluate the regional innovation system, an innovation 

index was formed based on internationally accepted principles. According to the comparative 

evaluation of the innovation potential of Azerbaijan, the innovation index was calculated for each 

region (Arzu Huseynova  2022, pp. 867-875). 

1.
Algorithm 
selection.

2. 

The choice of the 
cluster method, which 
determines the strategy 

of the process of 
merging objects into 

clusters (merging 
signs).

3. 

The selection of the 
method of calculating the 

inter-cluster distance, 
which determines the 
different aspects of 

objects united in clusters.

4.

Determini
ng the 

number of 
clusters.

estimation of innovation development of 
regions

factor analysis of innovation development 
of regions



Arzu HUSEYNOVA, Ophelya MAZANOVA 

 

91 

 

 This methodology has been refined considering the national and specific characteristics of 

Azerbaijan, statistical indicators in this field, information that can be collected and processed, and the 

innovation potential of the regions, the system of indicators has been changed and calculated for 

Azerbaijan. Calculations were made on 2 blocks (reserves and activity scale), 4 groups, and 14 

indicators. The special index indicator is denoted by Gijl where i=1, 2; j=1, 2; and l depends on the 

number of indicators in each group. 

 

Table 1. Division of the indicators system [Huseynova A.] 

Block Group Division of the indicators system 

Reserves 
Labor resources 4 

Material-technical base 2 

Scale 
Scientific activity 6 

Innovation activity 1 

 

 Data were collected and calculated according to the methodology we mentioned. The 

calculation results are not much different from previous years. This is proof that there was no great 

progress in this field in the regions, the situation has not changed. The obtained results are given in 

the table. 

 

Table 2. Innovation index by regions according to innovation development 

Regions 
On reserve group 

I1 

On scale group 

I2 

Regional innovation index 

I 

Baku 0,355347 0,40469 0,380019 

Nakhchivan 0,248108 0,228631 0,23837 

Mountainous 

Shirvan 0,312987 0,048597 0,180792 

Absheron 0,117459 0,234816 0,176137 

Ganja-Kazakh 0,109372 0,225259 0,167315 

Lankaran 0,143004 0,129577 0,13629 

Guba-

Khachmaz 0,087007 0,17371 0,130358 

Aran 0,120869 0,104973 0,112921 

Sheki-Zagatala 0,037363 0,145204 0,091283 

 

 As seen from the table, Baku is progressing in all groups. 

The regional innovation system consists of 3 subsystems: regional policy, scientific-

innovation policy, regional socio-economic policy.  

According to the methodology mentioned above, Huseynova A.D.(Huseynova 

A.D.&Mazanova O.I., 2015, p. 54–72) presented the methodology for evaluating the influence of the 

socio-economic environment on the innovation development of regions. The evaluation was carried 

out on 4 factors (innovation development level, education level, population welfare level and 

infrastructure development level). 

 

 



ESTIMATION OF REGIONAL INNOVATION ACTIVITY 

92 

 

Table 3. Factor index (Арсентьев А.С, 2010) 

№ Regions 

Innovation 

development 

level index 

Education 

level index 

Population 

welfare level 

index 

Infrastructure 

development level 

index 

1 Baku 0,91 1 1 0,75 

2 Absheron 0,50 0,28 0,21 1 

3 Nakhchivan 0,28 0,24 0,20 0,41 

4 Ganja-Kazakh 0,26 0,20 0,25 0,33 

5 Aran 0,18 0,03 0,17 0,36 

6 
Mountainous 

Shirvan 
0,17 0,03 0,13 0,33 

7 Lankaran 0,16 0,04 0,18 0,27 

8 
Sheki-

Zagatala 
0,16 0,03 0,17 0,27 

9 
Guba-

Khachmaz 
0,14 0,02 0,15 0,24 

 

As seen from the table, Baku is again sharply ahead. 

 The science and technology in Azerbaijan should be improved today. During the development 

of the national innovation system in the country, the development of scientific and technical potential 

and innovation in the regions is one of the essential issues.  

Let's analyze the indicators of science in Azerbaijan. 

 

Table 4. The main indicators of science by regions of the Republic of Azerbaijan 

Economic 

regions 

Number of 

ST 

organizations 

Number 

of ST 

employees 

(people) 

The 

volume of 

scientific and 

technical 

works 

performed 

during the year 

(thousand 

manats) 

Total 

expenses 

incurred by 

ST (thousand 

manats) 

Domestic 

expenses 

incurred by 

ST (thousand 

manats) 

ST cost 

of fixed 

assets used 

(million 

manats) 

on Azerbaijan 137 20 580 124 545,4 132 340,0 129 871,8 157,4 

Baku 102 16292 93 745,5 108 212,0 106 042,6 137,2 

Absheron 8 758 13 072,5 13 408,7 13 408,7 8,6 

Ganja-Kazakh 8 2 364 3 016,4 3 712,5 3 712,4 1,2 

Sheki-Zagatala 1 89 457,6 457,6 457,6 0,4 

Lankaran 3 93 284,6 284,8 284,8 0,1 

Guba-

Khachmaz 
2 140 759,2 759,2 759,2 1,3 

Aran 3 6 51,1 51,1 51,1 0,5 

Mountainous 

Shirvan 
2 156 1 124,0 1 124,0 825,3 - 

Nakhchivan 6 682 2 272,8 4 330,1 4 330,1 8,1 

 



Arzu HUSEYNOVA, Ophelya MAZANOVA 

 

93 

 

Analyzing the indicators of science in Azerbaijan, we observe that 76% of organizations 

engaged in scientific research are located in Baku.  

 For the calculation of the science index, it is necessary to bring the indicators given in Table 

7 to the same unit of measurement. In other words, let's normalize the indicators and calculate the 

science index based on the average value of the normalized values of these indicators. 

𝐸İ =
∑ 𝐸İ𝑖
𝑛
𝑖=1

𝑛
        (4) 

where, Eİi – is a i-th indicator included in the group, n- is the number of indicators. 

 

Table 5. Normalized values of science and science index by region 

Regions 
Number of ST 

organizations 

Number of ST 

employees 
(people) 

volume of 

scientific and 

technical 
works 

performed 

during the year 

(thousand 

manats) 

Total 

expenses 

incurred by 
ST (thousand 

manats) 

Domestic 

expenses 

incurred by ST 
(thousand 

manats) 

ST cost of 

fixed assets 

used (million 
manats) Science Index 

(SI) 

Baku 1 1 1 1 1 1 1 

Absheron 0,069307 0,046175 0,138977 0,123497 0,126025 0,062682 0,094444 

Ganja-Kazakh 0,069307 0,144787 0,031649 0,033851 0,034543 0,008746 0,053814 

Sheki-Zagatala 0 0,005096 0,004339 0,003758 0,003835 0,002915 0,003324 

Lankaran 0,019802 0,005342 0,002492 0,002161 0,002205 0,000729 0,005455 

Guba-Khachmaz 0,009901 0,008228 0,007558 0,006547 0,006681 0,009475 0,008065 

Aran 0,019802 0 0 0 0 0,003644 0,003908 

Mountainous Shirvan 0,009901 0,00921 0,011451 0,009919 0,007304 0 0,007964 

Nakhchivan 0,049505 0,041508 0,023712 0,039561 0,040371 0,059038 0,042283 

Note: Developed by the author  

 

 An unequal distribution of science in the republic and the low volume of scientific and 

technical works performed by regions during the year, the low share of innovation in the development 

of the economy of Azerbaijan's regions is a negative trend.  

 At the next stage, regression models showing the dependence between indicators included in 

different factor groups of innovation activity are built. As a result, equality was obtained for all 

blocks. As we know, regression analysis determines the relationship between dependent and 

independent variables. SPSS software was used to construct regression equations.  

 Here, the factor variable is given with the outcome variable in the input. But in the output: 

 

Figure 5. Output 

 
Source: author’s work 

 

 The obtained regression equations and statistics are given in table 4.3. A linear regression 

model was constructed for the following indicators: 

 G13 – Number of students per 1000 people; 

1. Correlation 
matrix;

2. Coefficients 
of regression 

equations;

3. Coefficients 
of R quadratic 

equations;

Values that 
determine the 

level of 
importance of 

the model.



ESTIMATION OF REGIONAL INNOVATION ACTIVITY 

94 

 

 G23 – unemployment rate, %; 

 G41 – Number of mobile phone subscribers per 1000 people. 

 

Table 6. Linear regression equations for the factors group  

A group of factors 
A linear regression 

equation 

Determination 

coefficient R 

Darbin-Watson 

coefficient DW 

Education level I=0,15+0,80Itəh R2=0,93 1,575 

The level of population 

welfare 
I=0,11+0,82Irif R2=0,76 0,831 

Level of infrastructure 

development 
I=0,03+0,60Iinf R2=0,56 1,530 

 

 Note that the coefficient of determination is completely dependent on the indicator of the 

innovation index, and since the Darbin-Watson coefficient is less than 2, it means that the 

autocorrelation is adequate for the indicators involved in the equation.  

 The coefficients of determination in the models of the dependence of the innovation index on 

the level of education, the level of welfare of the population, and the level of infrastructure 

development show that the innovation index depends on the indicators included in the model: the 

most on the level of education (93%), and the least on the development of infrastructure level (56%). 

 A multidimensional regression model was given. Here, the dependence of the innovation 

index with the indices calculated by factor groups is established: 

 

I = 0,337I1+ 0,332I2+0,329I3 +0,01       (5) 

Ii – is a factor groups index, ai – is their coefficient. 

DW=2; R2=1 

 When the coefficients of this equation are calculated, it is obtained that since the Darbin-

Watcon coefficient is equal to 2, autocorrelation is not possible for the indicators involved in the 

equation. Therefore, this model cannot be a worker. It means that there is no general dependence of 

the innovation index on the whole group of factors. 

 Let's form the model using stepwise regression. In this model, the variables are entered into 

the equation one by one, and the coefficient of determination is above R2=0,93 as a result of the first 

step regression model at each stage.  

 

I = 0,15+0,80Itəh        (6) 

where Iedu - are educational elements. 

 In the multidimensional regression model, the elements of education seem to be the main ones. 

Thus, 93% of the change in the innovation index depends on the educational elements of the region. 

 

CONCLUSIONS 

 We can conclude that the field of science and technology in Azerbaijan should improve today. 

In the period when the national innovation system was developing in the country, the development 

of innovation potential and innovation in the regions was one of the main issues. The formation of 

the national innovation system requires the development of regions. As a result of the research, two 



Arzu HUSEYNOVA, Ophelya MAZANOVA 

 

95 

 

methodologies were adapted to Azerbaijan and calculations were made: evaluation of innovation 

potential development and factor analysis methodologies of innovation potential development. Both 

are based on a system of indicators that characterize the internal and external environment and factors 

of innovation potential.  

 The proposed methods use widely applied tools in the international world. 

 In order to justify the indicators, the studies conducted in the world on the basis of inter-

country and inter-regional comparisons were studied, and based on those models, a system of 

characteristic indicators for Azerbaijan was selected and calculated. 

 The system of selected indicators allows us to evaluate the level of innovation development 

in different areas, and to analyze the factors affecting the innovation index in the regions.  

A methodical approach to the assessment of regional economic systems was proposed. The 

proposed approach considers the shortcomings of local experience and can be the basis for the 

development of management decisions related to innovation development and improvement of the 

efficiency of the economic system (Егорова М.В., 2009). 

 In the study, local and foreign methodological approaches to the assessment of regional 

innovation development were reviewed and analyzed, the general directions and general 

characteristics of methodical approaches to the assessment of regional innovation development were 

determined, and the innovation index was calculated for each region according to the comparative 

assessment of Azerbaijan's innovation potential.  

 The methodology used in the study was refined considering the national and specific 

characteristics of Azerbaijan, statistical indicators in this field, information that can be collected and 

processed, and the innovation potential of the regions, the system of indicators was changed and 

calculated for Azerbaijan. As a result of the conducted research, proposals were made to determine 

the role and competitiveness of the regions in the development of the economy of the republic. 

 The following results were obtained according to the results of the analysis of methodical 

approaches for the evaluation of the innovation development of the regions: 

 

Figure 6. Results 

 
Source: author’s work 

 

1.the estimation issue of the region innovation development should be developed by developing complex indicators and a 
necessary data collection system;

2.in the estimation of innovation development of the region, a comprehensive approach is appropriate, including expert 
assessment, considering quantitative and qualitative indicators;

3.it is necessary to consider the natural, demographic, and economic characteristics of the region. In regions with a 
majority of small nations, the impact of innovation on traditional lifestyles and traditional economic activities should be 
analyzed.

4.we should consider the natural, demographic, and economic characteristics of the region. In regions with a majority of small 
nations, the impact of innovation on traditional lifestyles and traditional economic activities should be analyzed.



ESTIMATION OF REGIONAL INNOVATION ACTIVITY 

96 

 

 Thus, a comprehensive estimation of regional innovation development should be developed. 

The results of this evaluation can be the basis for the improvement mechanism of the state policy. 

 The proposed model is an efficient tool for the analysis of RIS. It allows us to assess the 

origin and structure of resource flows, and to predict the risks that occur during the operation and 

development of the system under the influence of external factors. This also creates profiles of the 

regional innovation development to determine the individual characteristics of the territories. 

 

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